Choosing the Right ML Approach for Your Business Case with ISO/IEC 25053:2022

سرفصل های دوره

In this course, instructor Lyron Andrews shows you how to describe the system components of machine learning (ML) and their function in the AI ecosystem. This includes identifying general, supervised, unsupervised, and semi- or self-supervised learning, along with enumerating the steps related to the ML pipeline. Along the way, gather insights for evaluating the need for anomaly detection and dimensionality reduction related to training models. Lyron outlines the steps of the machine learning pipeline, from data acquisition and preparation to modeling, verification, and validation. By the end of this course, you'll be prepared to choose the right ML approach to meet the unique needs of your business case.

01 - Introduction

01 - Why the need for an ML process approach

02 - 1. Terms, Definitions, and Overview

01 - Overview of clauses, terms, and definitions (Clauses 3-5)

03 - 2. Machine Learning Systems

01 - Task and general (Clause 6-6.2)

02 - Task details (Clauses 6.2.2-6.2.7)

03 - ML model (Clause 6.3)

04 - ML data (Clause 6.4)

05 - Tools and data preparation (Clauses 6.5-6.5.2)

06 - Support vector machines ML (Clause 6.5.3.5)

07 - Bayesian ML (Clause 6.5.3.3)

08 - Decision tree ML (Clause 6.5.3.6)

04 - 3. Categories of ML Neural Networks

01 - General and FFNN ML (Clauses 6.5.3.2-6.5.3.2.2)

02 - RNN and LSTM ML (Clauses 6.5.3.2.3-6.5.3.2.3.2)

03 - CNN and CapNet ML(Clauses 6.5.3.2.4, 6.5.3.2.7)

04 - DBM, structured perceptron, and GAN ML (Clause 6)

05 - ML optimization methods (Clauses 6.5.4-6.5.4.8)

06 - ML evaluation metrics (Clauses 6.5.5-6.5.5.8)

05 - 4. Machine Learning Approaches

01 - General, supervised, and unsupervised (Clauses 7-7.3)

02 - Semi- and self-supervised (Clauses 7.4-7.5)

03 - Reinforcement and transfer (Clauses 7.6-7.7)

06 - 5. Machine Learning Pipeline

01 - Data acquisition and preparation (Clauses 8-8.3)

02 - Data preparation details (Clause 8.3)

03 - Modeling, verification, and validation (Clauses 8.5-8.6)

04 - Model deployment and operation (Clauses 8.6-8.7)

05 - Machine learning pipeline example (Clause 8.8)

07 - Conclusion

01 - Continue developing your ML business cases

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تولید کننده: LinkedIn Learning (Lynda)